Research Article
Research on Solving Hamiltonian Loop of Material Distribution Based on Ant Colony Algorithm
@INPROCEEDINGS{10.4108/eai.2-6-2023.2334611, author={Lixuan Liu and Zhixian Kong and Junrong Ma and Yaying Deng and Liangchuan Ma and Xingding Wu}, title={Research on Solving Hamiltonian Loop of Material Distribution Based on Ant Colony Algorithm}, proceedings={Proceedings of the 2nd International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2023, June 2--4, 2023, Nanchang, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2023}, month={8}, keywords={dijkstra algorithm genetic algorithm tsp problem}, doi={10.4108/eai.2-6-2023.2334611} }- Lixuan Liu
Zhixian Kong
Junrong Ma
Yaying Deng
Liangchuan Ma
Xingding Wu
Year: 2023
Research on Solving Hamiltonian Loop of Material Distribution Based on Ant Colony Algorithm
ICIDC
EAI
DOI: 10.4108/eai.2-6-2023.2334611
Abstract
With the increasing popularity of 5G communication technology, the high speed and low latency of 5G network provide information technology support for the development of the UAV industry. Nowadays, the material distribution mode of "delivery vehicle + drone" is widely used in many tasks such as disaster relief and cargo transportation. At present, in order to improve the transportation efficiency and reduce operating costs in this mode, it is particularly important to reasonably plan the distribution scheme of drones and vehicles. Based on the given location, route and distribution demand, the optimal distribution scheme is solved, the Dijkstra algorithm is used to calculate the shortest distance between any two points in the network diagram and the corresponding path to build a fully connected network, and then the genetic algorithm is used to solve the TSP problem with the shortest distance of the vehicle as the objective function, so as to approximate a Hamilton loop, and finally find the actual driving path of the vehicle.
